Closed-loop control of insulin infusion and system for measuring autonomic nervous system modulation

ABSTRACT

Disclosed herein are devices, methods and systems for monitoring and detection of adverse events in a subject. In an embodiment, an insulin delivery device includes an insulin injection device in communication with a controller for controlling the insulin injection device. The controller is configured to receive a heart signal from one or more heart sensors, and a blood glucose signal from one or more blood glucose sensors. The controller is further configured to analyze changes in the heart rhythm of the subject based on the heart signal and determine, based on the changes in the heart rhythm and the blood glucose signal, whether the subject is and/or will be experiencing an adverse event. Upon determination that the subject is or will be experiencing an adverse event, the controller determines one or more parameters of delivery of insulin to be delivered to the subject. Finally, the controller is configured to control the injection device to deliver insulin to the subject in accordance with the determined one or more parameters of delivery.

CROSS REFERENCE TO RELATED APPLICATION

The present application is the U.S. national stage to InternationalPatent Application No. PCT/IB2015/059902, filed Dec. 22, 2015, whichclaims the benefit of priority to U.S. Provisional Application No.62/095,195, filed Dec. 22, 2014, entitled “Closed-Loop Control ofInsulin Infusion”, the entire content of which is incorporated herein byreference.

TECHNICAL FIELD

This disclosure relates generally to monitoring and prevention of healthrelated conditions of a subject, and in particular, to monitoring andprevention of adverse events. This disclosure also relates generally topoint-of-care device, which can test and predict changes in theautonomic nervous system of a subject, and in particular, to a methodand apparatus for estimating the changes that may lead to adverse orbeneficial effects in the modulation of the autonomic nervous system.

BACKGROUND

Patients with diabetes are at a constant risk of hypoglycemia.Hypoglycemia often results in an increase in physical as well aspsychosocial morbidity, and is a risk factor for an increased mortality.Hypoglycemia is common in patients with type 1 diabetes (T1D). Patientstrying to improve or maintain a tight glycemic control suffer from alarge number of episodes of asymptomatic hypoglycemia. Plasma glucoselevels may be less than 60 mg/dl (3.3 mmol/l) 10% of the time, and onaverage, patients with T1D suffer from two weekly incidents ofsymptomatic hypoglycemia. Accordingly, patients with diabetes mayexperience thousands of hypoglycemic events over a lifetime. Inaddition, these patients have a 4.7-fold excess mortality risk comparedto healthy subjects. One of the approaches to mitigating these risks isthe use of continuous glucose monitoring (CGM) devices to detect andwarn diabetic patients about an imminent hypoglycemic event. However,problems such as false positive alarms continue to exist.

Autonomic nervous system (ANS) is a multifunctional system regulated bythe sympathetic nervous system and the parasympathetic system, providinga rapidly responding mechanism to control a wide range of bodilyfunctions such as cardiovascular, respiratory, gastrointestinal,genitor-urinary, exocrine and endocrine secretions, andmicrocirculation. Furthermore, ANS is involved in the regulation ofimmune and inflammatory processes. Autonomic dysfunction may affect boththe sympathetic nervous system and the parasympathetic nervous systemand may affect any organ that is innervated by the autonomic nervoussystem.

Heart rate (HR) and heart rate variability (HRV) are affected by bothinternal and external changes in, for example, breathing, bloodpressure, hormone status, mental condition and physical conditions. Anumber of pathophysiological conditions may shift the balance in the ANSthereby decreasing or increasing stimulation to the heart's sinoatrialnode, which controls HR and HRV. For example, increase in blood pressurecauses arteries to stretch, thereby causing increase in baroreceptordischarge frequency which, in turn, causes increase in parasympatheticand decrease in sympathetic activity. Similarly, carotid chemoreceptorstimulation by noradrenalin leads to slowing HR and increase in rate anddepth of respiration.

Recent studies have shown that screening for autonomic dysfunction theday before surgery may predict a blood pressure drop during anesthesia.Low blood pressure during anesthesia can cause critical ischemia ofvital organs like the brain and heart and should be treated quickly andeffective. It is shown in selected patient groups that preoperativedetermination of heart rate variability can predict drop in bloodpressure during anesthesia induction in patients with and withoutdiabetes. Previously conducted studies have had few participants andused equipment required special physical environment, which is whymeasurements are often carried out immediately before surgery. Tests ofthe autonomic function are not used consistently in consecutive androutine patient examinations to ensure that measurements are made at asafe time distance from the day of surgery, which will accompany mentalstress, known to affect the ANS negatively. Therefore the results ofthese previously conducted studies are dubious.

Test for autonomic dysfunction are based on measuring of heart rate andblood pressure during controlled exercise and breathing. In order tomake a diagnosis three active tests may be performed: 1) Heart rateresponse from laying to standing 1) Deep breathing to determine therelationship between heart rate during expiration and inspiration 3)Valsalva maneuver to determine heart rate during forced expiration andnormal breathing. Under all the above mentioned active tests theexternal stimuli (standing, deep breathing, forced expiration) changesvenous return to heart. The change in stroke volume (SV) stimulates thearterial baroreceptors by increasing/decreasing heart rate (HR) andtotal peripheral resistance (TPR) in an attempt to return arterial bloodpressure (BP) towards a normal homeostatic level as described by thefollowing equation: BP=SV×HR×TPR.

These three cardiovascular reflex tests combined with measurements ofblood pressure are commonly regarded as a gold standard for clinicalautonomic testing. If one of the three tests is abnormal, the patientsare diagnosed with autonomic dysfunction; if two or more tests areabnormal, the patient is diagnosed with autonomic neuropathy. Autonomicneuropathy is a very serious disease that usually occurs as acomplication of an underlying disease. The complication seen in manypatient groups, such as: Neurological disorders (Multiple sclerosis,Guillaine-Barre Syndrome, spinal cord injury) or Endocrine disorders(diabetes, Growth hormone disorders, Addison disease). Several publishedarticles demonstrating that autonomic dysfunction can predict coronaryheart disease, sudden death in patients with chronic heart problems.Elimination of risk factors for autonomic dysfunction (obesity, smoking,alcohol abuse, and hypertension) will delay or slow down the progressionof autonomic neuropathy. The recommended yearly screening of theautonomic nervous function is a quality assurance in clinical practice.For instance, impairment should produce an increased focus on riskfactors, including—but not only—glycaemic status, lipids and bloodpressure. Other closely associated diabetic complications to beconsidered are e.g. gastroparesis, impotence, retinopathy andneuropathy. Improvement, however, indicates that the patient's autonomicnervous system is well-functioning.

The composite physiological data may be collected with a plurality ofseparate measuring devices, each of which has measured the individualphysiological data such as exhale pressure, blood pressure and heartrate. Furthermore, some of these external devices are not appropriate totest the autonomic nervous system, which is sensitive to by bothinternal and external changes in for example mental condition andphysical conditions. The diagnosis has been based on simple lookup tableand does not calculate a prediction based on an algorithm.

SUMMARY

In an embodiment, a system for delivering a medicament to a subject isdescribed. The system may include one or more biomarker sensorsconfigured to measure a level of the biomarker of a subject, one or moreheart sensors configured to measure changes in a heart rhythm of thesubject, an injection device configured to deliver a medicament to thesubject; and a controller for controlling the injection device. Thecontroller may be in communication with the one or more heart sensors,the one or more biomarker sensors, and the injection device. Thecontroller may include a memory and one or more physical processorsprogrammed with instructions. The sensor and controller may be wearable,directly attached to the skin or placed nearby the measuring/infusionarea. The instructions when executed, cause the one or more physicalprocessors to receive a biomarker signal from the one or more biomarkersensors, and a heart signal from the one or more heart sensors, analyzechanges in the heart rhythm of the subject based on the heart signal,determine, based on the changes in the heart rhythm and the biomarkersignal, whether there is and/or will be a change in a physiologicalcondition of the subject, determine one or more parameters of deliveryof the medicament to be delivered to the subject, and cause theinjection device to deliver the medicament to the subject in accordancewith the determined one or more parameters of delivery.

In an embodiment, a method for determining if a subject is and/or willbe experiencing a hypoglycemic event is described. The method mayinclude analyzing changes in a heart rhythm of a subject, analyzing ablood glucose signal from a blood glucose sensor, the blood glucosesignal being an indicator of blood glucose levels of the subject, anddetermining, based on the changes in the heart rhythm and the bloodglucose signal, whether the subject is and/or will be experiencing ahypoglycemic event.

In an embodiment, a device for insulin delivery is described. The devicemay include an insulin injection device in communication with acontroller for controlling the insulin injection device. The controllermay be configured to receive a heart signal from one or more heartsensors, and a blood glucose signal from one or more blood glucosesensors, analyze changes in the heart rhythm of the subject based on theheart signal, determine, based on the changes in the heart rhythm andthe blood glucose signal, whether the subject is and/or will beexperiencing a hypoglycemic event, determine, based on the determinationthat the subject is and/or will be experiencing a hypoglycemic event,one or more parameters of delivery of insulin to be delivered to thesubject, and cause the injection device to deliver insulin to thesubject in accordance with the determined one or more parameters ofdelivery.

In an embodiment, a medicament delivery device is described. The devicemay include a medicament infusion module configured to deliver themedicament to a subject, and a controller for controlling the medicamentinfusion module. The controller may include a memory and one or morephysical processors programmed with instructions. The instructions whenexecuted, cause the one or more physical processors to receive abiomarker signal from one or more biomarker sensors, and a heart signalfrom one or more heart sensors, analyze changes in a heart rhythm of thesubject based on the heart signal, determine, based on the changes inthe heart rhythm and the biomarker signal, whether there is and/or willbe a change in a physiological condition of the subject, determine oneor more parameters of delivery of the medicament to be delivered to thesubject, and cause the medicament infusion device to deliver themedicament to the subject in accordance with the determined one or moreparameters of delivery.

In an embodiment, a device for predicting and detecting changes in theautonomic nervous system of a subject is disclosed. The device mayinclude one or more processors configured to (i) analyze dynamic changesin the heart rhythm of the subject during resting or during controlledexercise and breathing; (ii) analyze of one or more measurement, shownin table 4, linked to the autonomic nervous system from the subject; and(iii) combine an analysis of the dynamic changes in the heart rhythmwith an analysis of the one or more measurement to determine whetherthere is an adverse or beneficial effects in the autonomic nervoussystem in a time period.

In an embodiment, a method for predicting and detecting a change in theautonomic nervous system of a subject during resting or duringcontrolled exercise and breathing is disclosed. The method may includeanalyzing dynamic changes in the heart rhythm of the subject, analyzingone or more measurements linked to the autonomic nervous system obtainedfrom the subject, and combining analysis of dynamic changes in heartrhythm of a subject with analysis of changes in one or more measurementsobtained from the subject to determine whether there is an adverse orbeneficial effects in the autonomic nervous system in a time period.

In an embodiment, a system for predicting and detecting an adverse orbeneficial effect in the autonomic nervous system of a subject isdisclosed. The system may include one or more sensors configured tomeasure and record a heart rhythm of the subject; one or more sensorsconfigured to measure one or more parameters that are linked to theautonomic nervous system obtained from the subject, and one or moreprocessors. The one or more processors are configured to: (i) analyzedynamic changes in the heart rhythm of the subject, (ii) analyze one ormore measurements linked to the autonomic nervous system from thesubject, and (iii) combine an analysis of the dynamic changes in theheart rhythm with an analysis of the of the one or more measurements todetermine whether there is an adverse or beneficial effects in theautonomic nervous system in a time period.

BRIEF DESCRIPTION OF DRAWINGS

In the present disclosure, reference is made to the accompanyingdrawings, which form a part hereof. In the drawings, similar symbolstypically identify similar components, unless context dictatesotherwise. Various embodiments described in the detailed description,drawings, and claims are illustrative and not meant to be limiting.Other embodiments may be used, and other changes may be made, withoutdeparting from the spirit or scope of the subject matter presentedherein. It will be understood that the aspects of the presentdisclosure, as generally described herein, and illustrated in theFigures, can be arranged, substituted, combined, separated, and designedin a wide variety of different configurations, all of which arecontemplated herein.

FIG. 1 depicts an illustrative schematic of a control mechanism for aclose-loop artificial pancreas based on a glucose monitor signal, inaccordance with the principles and aspects of the present disclosure.

FIG. 2 depicts an illustrative schematic of a control mechanism for aclosed-loop artificial pancreas based on a glucose monitor signal and aheart rate signal, in accordance with the principles and aspects of thepresent disclosure.

FIG. 3 depicts an illustrative process for a method of monitoring andpredicting a change in a physiological condition using Heart RateVariability (HRV) in combination with one or more biomarkers, inaccordance with the principles and aspects of the present disclosure.

FIG. 4 depicts an illustrative pattern recognition model, in accordancewith the principles and aspects of the present disclosure.

FIG. 5 depicts a block diagram of a device used for analysis of HRVdata, in accordance with various aspects and principles of the presentdisclosure.

FIG. 6 shows an example of detection of hypoglycemia based on HRV dataand data from a Continuous Glucose Monitor (CGM) for a subject, inaccordance with the principles and aspects of the present disclosure.

FIG. 7 depicts an illustrative schematic of a feedback mechanism for aninsulin pump, in accordance with the principles and aspects of thepresent disclosure.

FIG. 8 depicts an illustrative schematic of a placement of glucosemonitor, ECG monitor and insulin pump on a subject's body, in accordancewith the principles and aspects of the present disclosure.

FIG. 9 depicts an illustrative schematic of a placement of glucosemonitor and insulin pump patches with built-in ECG electrodes andelectronics on a subject's body, in accordance with the principles andaspects of the present disclosure.

FIG. 10 depicts an illustrative schematic of a placement of glucosemonitor and insulin pump with external ECG electrodes on a subject'sbody, in accordance with the principles and aspects of the presentdisclosure.

FIG. 11 depicts an illustrative schematic of a placement of a singledevice functioning as glucose monitor, ECG monitor and insulin pump, ona subject's body, in accordance with the principles and aspects of thepresent disclosure.

FIG. 12 depicts an illustrative schematic of a patient patch for placinga glucose monitor and insulin pump with built-in ECG electrodes, inaccordance with the principles and aspects of the present disclosure.

FIG. 13 depicts a graph of continuous glucose measurements and periodicsingle glucose measurements obtained from one subject.

FIG. 14 depicts a time window used for predicting a single glucosemeasurement using continuous glucose measurements and heart variabilitydata.

FIGS. 15A-15C depict comparison of various parameters for models usingglucose only and glucose in conjunction with heart rate data forprediction of hypoglycemic events.

FIGS. 16A-16F schematically show a case story.

FIG. 17 show a block diagram for the point-of-care device.

FIGS. 18A-18B show an example for the handheld point-of-care device.

FIGS. 18C-18E show details of one of the electrodes of the handheldpoint-of-care device.

FIG. 19 shows an elementary sketch of a measurement of impedance formeasurement of pulse wave.

FIG. 20 shows an example of the mouthpiece.

FIGS. 21 to 23 shows different designs of the mouthpiece.

DETAILED DESCRIPTION

Before the present methods and systems are described, it is to beunderstood that this disclosure is not limited to the particularprocesses, methods and devices described herein, as these may vary. Itis also to be understood that the terminology used herein is for thepurpose of describing the particular versions or embodiments only, andis not intended to limit the scope of the present disclosure which willbe limited only by the appended claims. Unless otherwise defined, alltechnical and scientific terms used herein have the same meanings ascommonly understood by one of ordinary skill in the art.

It must also be noted that as used herein and in the appended claims,the singular forms “a”, “an”, and “the” include plural reference unlessthe context clearly dictates otherwise. Thus, for example, reference toa “sensor” is a reference to one or more sensors and equivalents thereofknown to those skilled in the art, and so forth. Nothing in thisdisclosure is to be construed as an admission that the embodimentsdescribed in this disclosure are not entitled to antedate suchdisclosure by virtue of prior invention. As used in this document, theterm “comprising” means “including, but not limited to.”

As used herein, the term “user” refers to a subject, human or animal,that uses the device or system disclosed herein. A user may be a personat risk for hypoglycemia such as, for example, a person having type I ortype II diabetes.

Disclosed herein are systems of devices in close proximity to a person'sbody that cooperate for the benefit of the user. The communication ofthese devices is known as body area network (BAN), or wireless body areanetwork (WBAN).

Disclosed herein are devices, methods and systems for monitoring anddetection of information embedded in the autonomic nervous system in theheart rhythm of an individual. The methods disclosed herein may befurther used during normal living (e.g., fasting, eating, activity,daily stress, etc.) because they are independent of ectopic beats,arrhythmia and artifacts which may normally limit the robustness ofsimilar devices.

Disclosed herein are devices, methods and systems for monitoring anddetection of adverse events such as hypoglycemia, hyperglycemia, ordevice safety issues during automated delivery of medication. Thedevices, method and systems disclosed herein may be further used forprevention of these events by controlled infusion of insulin inanticipation of an event, and transmitting this information to the useror a person associated with the user (e.g., a relative, or a caregiver).

An “open-loop system” e.g. a subcutaneous insulin pump with real-timecontinuous glucose monitoring (CGM) is currently being used for themanagement of type 1 diabetes in selected individuals. The limits of theopen loop system are particularly seen in pediatric populations and inindividuals with less motivation or with cognitive impairment.Furthermore, open-loop systems suffer from user errors, poor detectionof alarms during sleep, and complacency with frequent alarming forhypoglycemia are problems with the current systems. These issues supportthe need for the development of control algorithms that automaticallyand accurately alter insulin infusion rates to achieve normal glucoselevels during fasting, eating, activity, and daily stress. These andother drawbacks exist.

FIG. 1 depicts a schematic of an automated mechanical glucose-responsivesensor-guided insulin infusion system also called an artificial pancreasor a “closed loop system.” A closed-loop system may include, (asdepicted in FIG. 1): a continuous glucose monitoring (via a subcutaneoussensor or noninvasive e.g. Smart lens) device; a computerized closedloop controller to determine the proper insulin infusion rate andautomatically adjusting insulin levels in a subject; and a subcutaneousinsulin pump.

FIG. 2 depicts a schematic of a closed-loop artificial pancreas that iscontrolled based on a continuous glucose monitor signal and a heart ratesignal. A computerized closed-loop controller determines advent ofhypoglycemic events and adjusts insulin infusion rates so as toautomatically adjust blood glucose levels in a patient. The insulin maybe provided to the patient via, for example, a subcutaneous insulinpump.

In an embodiment, hypoglycemic events may be detected using changes inheart rate and heart rate variability (HRV) in conjunction withcontinuous glucose monitor signals. Advantageously, using heart rate andheart rate variability in conjunctions with continuous glucosemonitoring as described herein improves detection of hypoglycemic eventsduring normal living (e.g., during fasting, eating, activity, dailystress, etc.).

As used herein, “heart rate variability” (HRV) refers to variation inthe time interval between heartbeats. HRV has been found to be a measureof the balance in the autonomic nervous system and is dependent on bothinternal and external changes in the body. Decreased parasympatheticnervous system activity or increased sympathetic nervous system activityresults in reduced HRV. HRV may be measured using, for example,electrocardiogram, blood pressure, ballistocardiograms, pulse wavesignals derived from photoplethysmograph, and so forth. In variousembodiments, HRV may be measured at different sampling rates such as,for example, 0.01 Hz, 0.05 Hz, 0.1 Hz, 0.5 Hz, 1 Hz, 5 Hz, 10 Hz, 50 Hz,100 Hz, 500 Hz, 1 kHz, and so forth or at any sampling rate between anytwo of these sampling rates.

By combining the complex dynamic/pattern of HRV with a surrogate measureof a biomarker it may be possible to improve the detection andprediction of a given change in a physiological condition which ismeasured by the biomarker surrogate. The HRV dynamic/pattern addsimportant information regarding the modulation of the autonomic nervoussystem and thereby can be used to clarify whether a change or eventmeasured by the biomarker is of physiological significance, which couldinclude a change or event of clinical interest that might requireclinical intervention. This clarification is more significant when usinga surrogate measure of a biomarker. For example, when the biomarkersurrogate is CGM, there is a lag-time between CGM measurements andactual blood glucose levels (glucose levels in interstitial fluid lagbehind blood glucose values) causing poor accuracy in event detection.Therefore, in terms of detection of hypoglycemia or hyperglycemia, CGMdevices, have poor specificity and thus result in numerous falsepositive alerts. By combining pattern recognition of HRV with a CGMdevice the detection and prediction of hypoglycemia or hyperglycemia maybe significantly improved. Besides detection and prediction ofhypoglycemia or hyperglycemia the methods disclosed herein may be usedin any biomarker surrogates that are influenced by the autonomic nervoussystem.

FIG. 3 depicts an illustrative process for a method of monitoring andpredicting a change in a physiological condition using Heart RateVariability (HRV) in combination with one or more biomarkers accordingto an embodiment. At block 110, HRV of a subject is measured by asensor. The HRV data fed to a processor P which, at block 150, analyzesthe HRV data based on a pre-determined algorithm. At block 130,processed HRV data is combined (using, e.g., another processor not shownin FIG. 1) with measurements relating to one or more biomarkers BIOMfrom one or more sensors gathered at block 120 and analyzed for changein a physiological condition. This analysis may be fed back to processorP for analysis at block 150. If the change in the physiologicalcondition is deemed, based on a pre-determined set of criteria, areaction R is provided at block 175.

In various embodiments, the patterns in the HRV data may be used toevaluate the clinical relevance of each data point obtained from thebiomarker measurements. For example, in an embodiment, glucosemeasurement is used for detection of hypoglycemia. In such embodiment,glucose levels are measured periodically (e.g., every 5 minutes) andpatterns in HRV data are used to determine whether a particular glucosemeasurement indicates an onset of hypoglycemia. In other embodiments,other biomarkers may be used and measurements obtained at a differentfrequency. In some embodiments, the biomarker data may undergoprocessing similar to the HRV data.

In various embodiments, physiological conditions may be induced undercontrolled clinical conditions while gathering HRV data. In manyembodiments, HRV data may be gathered for up to 10 hours prior toinduction of the physiological event and up to 10 hours after theinduction of the physiological event. As such, incidence of variousfeatures and patterns extracted from the HRV data may be correlated withthe particular physiological event being induced based on the analysisbeing performed.

HRV of a subject may be measured using any device or method. Forexample, in an embodiment, HRV of a subject is measured usingelectrocardiogram (ECG). FIG. 4 depicts an illustrative patternrecognition model according to an embodiment. In various embodiments,analysis of HRV at block 150 may include, for example, preprocessing atblock 210, feature extraction at block 220, feature reduction at block230, and classification at block 240.

In embodiments where HRV is measured using ECG, a signal from the ECG ispreprocessed, at block 210, for detection of peaks and calculation ofRR-intervals. RR-interval, as used herein, is the interval between an Rwave and the next R wave as measured by the ECG. The R-wave detectionmay be performed with various methods such as, for example, Pan andTompkins with (a) bandpass filter, (b) differentiating, (c) squaring and(d) moving-window integration or signal energy analysis andmoving-window.

In various embodiments, the verification of RR recording may beperformed using one or more of the analysis tools such as, for example,Poincare Plots, Nonlinear analysis, or time-frequency analysis, and maybe performed in time domain or frequency domain. Power spectra densitymay then be estimated using parametric or non-parametric models such as,for example, Welch's method, auto regression, periodogram, Bartlett'smethod, autoregressive moving average, maximum entropy, least-squaresspectral analysis, and so forth.

In various embodiments, RR-intervals may be divided in epochs of severalminutes. It will be understood by one skilled in the art that any timelength of an epoch may be chosen and will depend on factors such as, forexample, data sampling rate, processing power, memory available to theprocessor, efficiency of algorithms used for analysis, and so forth. Inan embodiment, for example, duration of an epoch may be 5 minutes.

RR-interval outliers from each epoch may then be replaced with a meanfrom that particular epoch. Outliers, in some embodiments, may bedefined as RR-intervals deviating 50% from previous data RR-interval oroutside 3 standard deviations. Epochs may be analyzed using proprietaryor commercially available tools. The analysis may be performed using oneor more of analysis tools such as, for example, of Poincare Plots,Nonlinear analysis, time-frequency analysis and performed in time domainor frequency domain. Power spectra density may then be estimated usingparametric or non-parametric models such as, for example, Welch'smethod, auto regression, periodogram, Bartlett's method, autoregressivemoving average, maximum entropy, least-squares spectral analysis, and soforth.

Preprocessing of the ECG signal may be followed by feature extraction,at block 220. Preprocessed RR-interval data is sent to block 220 tofind, preferably, a small number of features that are particularlydistinguishing and/or informative for classification of the featuresbased on physiological conditions being induced. In various embodiments,features extracted, at block 220, from the RR-interval data up toseveral epochs prior to the physiological event may be used forcalculating various features. In some embodiments, analysis may beperformed on data, for example, 10 epochs, 15 epochs, 20 epochs, 30epochs, 40 epochs, 50 epochs, 100 epochs or any number of epochstherebetween, prior to the physiological event.

Analysis performed on the RR-interval data at block 220 may, in variousembodiments, include, for example, differentiation, averaging,calculation of slope, ratios of instantaneous values, standarddeviation, skewness, regression coefficients, slopes of regressionratios, and standardized moment, and so forth. Features extracted fromthe HRV data may include, for example, median heart rate average fromparticular epoch range prior to an event, or the skewness of standarddeviation of normal-to-normal intervals from particular epoch rangeprior to an event, and so forth. In some embodiments, analysis of HRVmay be performed in real-time during daily living, or in combinationwith control exercise or paced breathing.

RR-interval data extracted at block 220 may include a large number ofdifferent features may be evaluated for their ability to discriminatefor a physiological event. Such features may then, be passed down toblock 230 to be grouped to form patterns that may be indicative of aparticular physiological event. At block 230, a ranking algorithm basedon e.g. a t-test may be used, in some embodiments, for eliminatingfeatures that do not signify an event.

In some embodiments, the ranking algorithm may calculate an averageseparability criterion for each feature. Such a criterion may reflectthe ability of the classification method to separate the means of anytwo classes of features in relation to the variance of each class.Subsequently, various features may be correlated with physiologicalevents. Features with lowest separability may be eliminated ifcorrelation with higher ranking features exceeds a threshold. In anembodiment, a correlation threshold of, for example, 0.7 may be used. Invarious embodiments, the correlation threshold may be chosen dependingon the desired specificity and sensitivity of prediction of thephysiological event. In many embodiments, cross-validation may beperformed to reduce generalization errors.

Once the features are extracted and reduced, particular features may bechosen for their ability to predict a physiological event based oncorrelation factors. This is followed by classification, at block 240,of the features to correlate them with particular physiological events.Various classification models may then be used for classifyingphysiological events as normal or abnormal based on such features. Forexample, in an embodiment, non-probabilistic binary linear classifiersupport vector machine may be used. A skilled artisan will appreciatethat other classification methods may be also used, alone or incombination. For example, linear classifier models such as Fisher'slinear discriminant, logistic regression, naive Bayes classifier,Perceptron, may be used for classification. Other examples ofclassification models include, but are not limited to, quadraticclassifiers, k-nearest neighbor kernel estimation, random forestsdecision trees, neural networks, Bayesian networks, Hidden Markovmodels, Gaussian mixture models, and so forth. In some embodiments,multi-class classification may also be used, if needed.

In an embodiment, at block 240, forward selection may be used to selecta subset of features for optimal classification. This selection may beperformed by including a cross-validation with, for example, 10 groupsand allocating a particular number of events for training the model.Forward selection may start with no features followed by assessing eachfeature to find the best feature that correlates with the particularphysiological event. Such feature may, then, be included in an optimalfeature subset for appropriate classification. Selection of new featuresmay be repeated until addition of new features does not result inimproved predictive performance of the model.

FIG. 5 depicts a block diagram of a device used for analysis of HRV datain accordance with various aspects and principles of the presentdisclosure. Device 300 used for analysis of HRV data may includeprocessor 310 configured to run algorithm 320 that enables prediction ordetection of a physiological event. Heart rhythm 350 along with at leastone biomarker 375 and their time of measurement are received andanalyzed by algorithm 320. In some embodiments, measurements of heartrhythm 350 and biomarker 375 may be entered manually. In otherembodiments, the measurements may be transmitted automatically toprocessor 310 using a wired or a wireless connection to device 300.Algorithm 320 may include, calculating one or more statistical measures,at block 322, of heart rhythm 350 and biomarker 375 data. At block 324,the physiological state or change in the physiological state isestimated and analyzed for a possibility that the physiological state orchange in the physiological state may be non-healthy. At block 326, anoutput is generated based on the analysis of block 324. For example, ifit is determined, at block 324, that a change in physiological state isnon-healthy, an alarm signal is generated at block 326. Device 300 mayproduce a reaction 340 based on the output generated at block 326. Invarious embodiments, reaction 340 may be a visual, audio, or audiovisualsignal such as, for example, an alarm, a text message, a flashing light,and so forth.

In many embodiments, processor 310 may be part of a computer, a tablet,a smartphone, or a standalone device. In some embodiments, the devicemay have in-built sensors for measuring HRV data 350. For example, asmartphone having a light emitting diode (LED) capable of producinginfra-red light and an optical sensor (e.g., a camera) may be able toobtain HRV data using IR thermography. In many embodiments, the deviceused for analyzing the HRV data may include, for example, a controllingunit (e.g., a digital signal processor or DSP), a memory (e.g., randomaccess memory, and/or non-volatile memory), one or more sensors (e.g.,IR sensors, electrodes, etc.), one or more feedback mechanisms (e.g.,display, a printer, speakers, LEDs or other light sources, etc.), and/orone or more input ports. The device for analyzing HRV data may alsoinclude sensors for measuring and analyzing any other biomarker(s).

In an embodiment, HRV measurements 350 may be combined, at block 322,with measurements of blood glucose levels 375 for monitoring andprediction of hypoglycemia. In such embodiments, HRV data 350 may becombined with, e.g., blood glucose measurements 375 taken over a periodof time prior to a hypoglycemic event. Patterns from the combination ofHRV and blood glucose data may be used to discriminate betweennormoglycemia and hypoglycemia. A model may be trained by analyzing HRVfeatures over, e.g., 10-20 epochs combined with blood glucosemeasurements prior to an induced hypoglycemic event. Once trained todiscriminate between normoglycemic events and hypoglycemic events, themodel may then be used to predict, at block 324, the occurrence of ahypoglycemic event based on HRV and blood glucose measurements.

Blood glucose data 375 may be obtained intermittently or continuously.In some embodiments, it may be possible to obtain blood glucose datausing non-invasive technologies that include, for example, infra-reddetection, ultrasound or dielectric spectroscopy and so forth. In manyembodiments, such technologies may be integrated with equipment used forobtaining HRV data. In other embodiments, an implanted chip may be usedfor obtaining continuous blood glucose data.

Table 1 provides a list of biomarkers that may be used in concert withHRV for predicting and monitoring various physiological conditions.

TABLE 1 Examples of Biomarker or surrogate measure of Physiologicalcondition a biomarker Epileptic attack EEG, EMG, motion detection Asthmaattack EEG, breathing (sounds and rate) Panic attack EEG, ECG, breathing(sounds and rate), sudomotor function Heart attack or event ECG, pulsewave velocity Sudden hypotension Blood pressure Sleep apnea EEG,breathing (sounds and rate) Fatigue ECG, EMG, motion detection Stress,including post- EEG, bioimpedance, breathing (sounds and rate) traumaticstress Neuropathy Bioimpedance, nerve conduction, EEG, EMG, dolorimeter,vibration testing, motion detection Dehydration Blood pressure,bioimpedance, breathing Liveness detection Bioimpedance Lie detectionPolygraph List of biomarkers or biomarker surrogates used fordetection/prediction for physiological events/conditions(EEG-electroencephalogram; EMG-Electromyography;ECG-Electrocardiography).

Another embodiment is implemented as a program product for implementingsystems and methods described herein. Some embodiments can take the formof an entirely hardware embodiment, an entirely software embodiment, oran embodiment containing both hardware and software elements. Oneembodiment is implemented in software, which includes but is not limitedto firmware, resident software, microcode, etc.

Furthermore, embodiments can take the form of a computer program product(or machine-accessible product) accessible from a computer-usable orcomputer-readable medium providing program code for use by or inconnection with a computer or any instruction execution system. For thepurposes of this description, a computer-usable or computer readablemedium can be any apparatus that can contain, store, communicate,propagate, or transport the program for use by or in connection with theinstruction execution system, apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device). Examples ofa computer-readable medium include a semiconductor or solid-statememory, magnetic tape, a removable computer diskette, a random accessmemory (RAM), a read-only memory (ROM), a rigid magnetic disk, and anoptical disk. Current examples of optical disks include compactdisk-read only memory (CD-ROM), compact disk-read/write (CD-R/W), andDVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

The logic as described above may be part of the design for an integratedcircuit chip. The chip design is created in a graphical computerprogramming language, and stored in a computer storage medium (such as adisk, tape, physical hard drive, or virtual hard drive such as in astorage access network). If the designer does not fabricate chips or thephotolithographic masks used to fabricate chips, the designer transmitsthe resulting design by physical means (e.g., by providing a copy of thestorage medium storing the design) or electronically (e.g., through theInternet) to such entities, directly or indirectly. The stored design isthen converted into the appropriate format (e.g., GDSII) for thefabrication.

The resulting integrated circuit chips can be distributed by thefabricator in raw wafer form (that is, as a single wafer that hasmultiple unpackaged chips), as a bare die, or in a packaged form. In thelatter case, the chip is mounted in a single chip package (such as aplastic carrier, with leads that are affixed to a motherboard or otherhigher level carrier) or in a multichip package (such as a ceramiccarrier that has either or both surface interconnections or buriedinterconnections). In any case, the chip is then integrated with otherchips, discrete circuit elements, and/or other signal processing devicesas part of either (a) an intermediate product, such as a motherboard, or(b) an end product.

FIG. 7 depicts a feedback mechanism for controlling an insulin pump. Inan embodiment, a closed-loop artificial pancreas may be implemented witha computerized controller that uses the pattern of HRV and CGM signalsfor controlling an infusion rate of insulin via an insulin pump. In suchan embodiment, the computerized controller implements an algorithmdescribed herein to predict onset of adverse events such ashypoglycemia, hyperglycemia, or device safety issues during automateddelivery of insulin. For example, the computerized controller may shutof insulin infusion if the algorithm predicts that a hypoglycemic eventis impending and send notification to the patient, an emergencyresponder, or a caregiver associated with the patient. In variousembodiments, the controller may additionally contain a GPS trackingsensor. In such embodiments, the notification may include the locationof the patient so that a caregiver or an emergency responder can locatethe patient with relative ease.

In an embodiment, a closed-loop artificial pancreas system may include aglucose monitor, an ECG monitor and an insulin pump placed on asubject's body. FIG. 8 depicts a relative placement of the glucosemonitor, the ECG monitor and the insulin pump on the subject's body. Insuch an embodiment, the glucose monitor may be a continuous glucosemeasurement monitor, which includes sensors for collecting glucose dataand electronics for analyzing the collected glucose data. The ECGmonitor may include electrodes and electronics for collecting andanalyzing heart rate and heart rate variability signals as describedelsewhere herein. The insulin pump may include microfluidic channels forappropriately delivering insulin (e.g., through subcutaneous infusion)to the subject, and electronics for controlling the rate of flow ofinsulin via the microfluidic channels. The glucose monitor and the ECGmonitor may be connected to the insulin pump via wired or wirelessconnections.

In an embodiment, a closed-loop artificial pancreas system (which, insome embodiments, may be a wearable device) may include a glucosemonitor and an insulin pump having built-in ECG electrodes. FIG. 9depicts a relative placement of the glucose monitor and the insulin pumpin such an embodiment. The insulin pump of such an embodiment mayinclude patches with built-in electrodes for ECG measurements, therebyminimizing the area of the body where the wearable device is attached,leading to better compliance and patient comfort. The glucose monitorand the insulin pump may be connected via a wired (as depicted) or awireless connection. The electronics for analyzing the ECG and CGM data,and controllers for controlling the delivery of insulin maybe integratedwithin the insulin pump.

In an embodiment, the insulin pump may include electronics forcollecting and analyzing the ECG data, electronics for analyzing thecombination of the ECG data and the CGM data, and controllers forcontrolling the delivery of insulin. In such an embodiment, the ECGelectrodes may be external to the insulin pump (as depicted in FIG. 10).The glucose monitor may be connected to the insulin pump via a wired ora wireless connection.

In an embodiment, the glucose monitor, the ECG monitor, and the insulinpump may be integrated within a single device, as depicted in FIG. 11.In such an embodiment, needle(s) and/or tube(s) for subcutaneousdelivery and/or measurement of insulin may double as electrodes forcollecting the ECG data. In another embodiment, a patch or surface ofthe CGM monitor or the insulin pump may double as the ECG electrodes.Such embodiments are depicted in FIG. 12. In an embodiment, an ECGelectrode can be used for delivering and/or measuring insulin and/orglucose levels.

In an embodiment, bio-potential electrodes may be used for collectingECG data. The bio-potential measurement, e.g., a voltage produced by atissue of the body (e.g., a muscle tissue during a contraction) may beperformed with conductive or non-conductive electrodes. Advantageously,non-conductive electrodes or capacitive electrodes do not need directcontact between skin and electrodes, thereby saving the time needed toexpose and prepare the contact area when measuring with conductiveelectrodes. Non-conductive electrodes, however, may be more sensitive toeffects of motion than conductive electrodes.

Bio-potential electrodes may consist of multiple layers of metal. Forexample, a first layer may be optimized to bond the housing of theartificial pancreas system (e.g. Cu on ABS plastic), a second layer maybe primer electrode material (e.g., silver), and a third layer may be anoptimization of the electrode material for improving the impedancematching between electrodes and the measuring objectives. In anembodiment with a silver primer electrode, the electrode may comprisesilver-silver chloride (Ag/AgCl). In one embodiment the first bindinglayer may not be present. In one embodiment the third layer may be adisposable tape.

In various embodiments, the bio-potential-electrodes can be incorporatedor embodied into any material like metal or foam material such asrubber, textile, or plastic. For optimized performance and electricalproperties of electrodes it may be advantageous to add noble colloidalmetal (e.g. silver, gold or platinum) particles suspended in a liquiddirectly to the bio-potential electrodes, or into the conducting gel, orby applying it directly to the skin of the subject before examinations.In such embodiments, the colloidal metal may improve the signal/noiseratio by reducing the skin impedance. Dry and/or old skin creates highimpedance which makes it difficult to acquire good readings. The use ofcolloidal metal may eliminate the need for preparation of the patients'skin by lowering skin impedance.

In one embodiment the colloidal metal or other conducting material likechloride gel or a combination with colloidal and chloride may also beadded to the adhesive patch from the CGM and insulin pump as depicted inFIG. 12.

The surface coated bio-potential electrodes in combination with the useof colloidal metal are not limited to ECG electrodes described herein,and many other applications of biological electrode systems such asbiomonitoring electroencephalography (EEG), and Electromyography (EMG)may use such electrodes.

In one embodiment the glucose monitor, the ECG monitor, and the insulinpump may by combined with one or more of the wearable's sensors, shownin table 2, such as motions, breathing, EMG or EEG sensors.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes by the use of diagrams, flowcharts, and/orexamples. Insofar as such diagrams, flowcharts, and/or examples containone or more functions and/or operations, it will be understood by thosewithin the art that each function and/or operation within such diagrams,flowcharts, or examples can be implemented, individually and/orcollectively, by a wide range of hardware, software, firmware, orvirtually any combination thereof.

Those skilled in the art will recognize that it is common within the artto describe devices and/or processes in the fashion set forth herein,and thereafter use engineering practices to integrate such describeddevices and/or processes into data processing systems. That is, at leasta portion of the devices and/or processes described herein can beintegrated into a data processing system via a reasonable amount ofexperimentation.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermediate components.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

All references, including but not limited to patents, patentapplications, and non-patent literature are hereby incorporated byreference herein in their entirety.

Embodiments illustrating the devices, methods and systems describedherein may be further understood by reference to the followingnon-limiting examples:

Examples Detection of Hypoglycemia Based on Heart Rate Variability andCGM Data

Embodiments described in the examples may utilize the devices, methodsand processes described herein with respect FIGS. 3-5.

Data Collection:

Data from 10 patients was obtained. 10 adults with type I diabetes wererecruited for studies into hypoglycemia under clinical settings. None ofthe patients with diabetes had a history of cardiovascular disease. Noneof the patients were taking drugs affecting the cardiovascular system,and all had normal electrocardiograms.

On the study day, hypoglycemia was induced by a single subcutaneousbolus of insulin aspart. Subjects were placed in a hospital bed with theback rest elevated to a comfortable position. Equipment for measuringthe ECG (lead II) and a CGM device (Guardian RT, Minimed, Inc.,Northridge, Calif.) producing a reading every 5 minutes were mounted andan intravenous cannula was placed in an antecubital vein in bothforearms. Blood samples for measurements of insulin were taken at thebeginning and end of a baseline period. Throughout the experiment, bloodglucose measurements were obtained frequently from earlobe capillaryblood.

Blood glucose readings were spline resampled with a rate of 5 minutesequivalent to each reading of the CGM device. The blood glucose readingswere used as reference for periods/events with hypoglycemia andcategorize events as (i) hypoglycemia—defined as the point of time of ablood sample closest to the value of 3 mmol/l glucose, andnormoglycemia—defined as the point of time of a blood sampleapproximately 1 hour prior to the hypoglycemic event.

Once the HRV and blood glucose data is collected, all data processingwas performed using custom analysis software developed in MATLAB R2011b(Mathworks, Natick, Mass.).

Preprocessing:

The ECG V5 signal was used for detection of peaks and calculation ofRR-intervals. RR-intervals were divided in epochs of 5 minutes duringthe trial. RR-interval outliers from each epoch were replaced with themean from that particular epoch. Outliers were defined as RR-intervalsdeviating 50% from previous data RR-interval or outside 3 standarddeviations.

Epochs were analyzed using HRV analysis software (HRVAS) module and 103measures ranging from time domain, Poincare, Nonlinear, time-frequencyto frequency domain were derived from the epoch. Two different modelswere used to estimate power spectra density: welch and auto regression.

Feature Extraction and Reduction:

In short, feature extraction and reduction is performed to findpreferably small number of features that are particularly distinguishingor informative for the classification. Measures derived from RR-intervalepochs 10-40 prior to an event were used for calculating multiplefeatures. Table 2 shows calculations used to combine different epochmeasures.

TABLE 2 List of equations used to combine HRV measures into features.Description Equation Differentiation M_(y)epc_(x1)-M_(y)epc_(x2)Averaging μ (M_(y)epc_(x1 . . .) M_(y)epc_(xn)) Slope α(M_(y)epc_(x1 . . .) M_(y)epc_(xn)) Standard deviation σ(M_(y)epc_(x1 . . .) M_(y)epc_(xn)) Skewness Υ₁ (M_(y)epc_(x1 . . .)M_(y)epc_(xn)) Ratio M_(y)epc_(x1)/M_(y)epc_(x2) M_(y) represent a HRVmeasure y, epc_(x1) represent an 5 minutes RR-interval epoch, μ is thearithmetic mean, α is the slope regression coefficients, σ is thestandard deviation, Υ1 is the third standardized moment.

Example of features could be (a) the median heart rate averaged fromepochs 10-20 prior to an event or (b) the skewness of standard deviationof Normal-to-Normal intervals (SDNN) from epochs 10-40 prior to anevent.

To classify the patterns, 3296 different features were evaluated fortheir discrimination abilities. A ranking algorithm based upon a t-testwas used to eliminate features. The ranking algorithm calculates anaverage separability criterion for each feature, which is the ability toseparate the means of the two classes in relation to the variance ofeach class. The features are then correlated, and the feature with thelowest separability criteria is eliminated if correlation with higherranking features exceeds the threshold. In this study a correlationthreshold of 0.7 was used. To ensure that the features obtained wouldnot be over fitted, i.e. to reach a low generalization error, across-validation method was used. A total of the best 20 features wereused to inclusion in the classification model.

Classification Model:

Non-probabilistic binary linear classifier Support vector machines (SVM)was used to classify the events of normoglycemia and the events ofhypoglycemia. First a forward selection method was used to select asubset of features for the optimal classification model. This selectionwas done including a cross-validation with ten groups, leaving 2 eventsout for classification and 18 events for training. Forward selectionstarts with no features and assesses every single feature and finds thebest feature. This feature is then included as part of the optimalfeature subset. All other features are added again to form a two-featuresubset etc. this is repeated until new features doesn't increase theperformance.

A subset of features selected is then used for the final classificationmodel, which also included a ten-fold cross-validation. Sensitivity andspecificity is used to evaluate the classification model.

Results:

A total of 903 samples equivalent to 4515 minutes among 10 patients with16 hypoglycemic events were analyzed and classified using the model. Thesample-based evaluation yielded a ROC AUC of 0.98. With a specificity of99% the model had a sensitivity of 79%. This is a significantimprovement over CGM alone. Event-based, the model classified allhypoglycemic events correctly, did not detect any false-positive eventsand had a lead-time of 22±(12) minutes. CGM alone was able to detect 12out of 16 events with a lead-time 0±(11) minutes. FIG. 6 shows the datafor one patient where CGM alone detects hypoglycemia, indicated by 430,late after the real onset, indicated by 410, while the present methoddetects the hypoglycemic event, indicated by 420, one minute after thereal onset.

Example 2: Prediction and Improved Detection of Spontaneous HypoglycemicEvents Methods

Participants:

A total of 21 (13 men and 8 women) adults with long lasting T1D wererecruited. The patients were 58±10 years old, had a diabetes duration of34±12 years and a HbA1c 7.9±0.7%, and 11 participants had peripheralneuropathy measured by biothesiometer. All participants were prone tohypoglycemia, i.e. they had experienced at least two episodes of severehypoglycemia within the last year. None of the patients had a history ofcardiovascular disease or were taking drugs affecting the cardiovascularsystem. All patients had a normal electrocardiogram. The study protocolswere approved by the local ethics committee and the study conductedaccording to the principles of the “Helsinki Declaration II”. Allpatients gave their written informed consent.

Study Design:

ECG was measured from lead II using a digital Holter monitor (SpiderViewPlus; ELA Medical, Montrouge, France). At the same time, CGM wasmonitored using a Guardian Real-Time Continuous Glucose MonitoringSystem (Medtronic MiniMed, Northridge, Calif., USA) with the prevailingglucose level blinded. At 11 PM a cannula was placed into an antecubitalarm vein. Blood glucose samples were taken at hourly intervals until 7AM the next morning. At 8 AM, participants were sent home with themonitoring equipment and they were instructed to calibrate the CGM atleast four times a day. Monitoring ended on Sunday at 8 PM. A total of72 hours of continuous CGM and ECG data were available for eachparticipant.

Data Processing:

The ECG was analyzed using custom analysis software developed in MatLab(Version R2014a; MathWorks, Natick, Mass., USA). ECG QRS detection wasimplemented based on the methods of Pan and Tompkins with (a) bandpassfilter, (b) differentiating, (c) squaring and (d) moving-windowintegration (19). Initial R-peaks were identified with a threshold and aminimum time distance of 250 ms from the moving-window integrationoutput. R detections were then found as the highest point in theoriginal signal within the timeframe of the initial detected peak.Inter-beat intervals were derived from the R detections andinterpolation was used to remove outliers based on 2·StdHRV. Thefiltered HRV signal was inspected manually and periods with substantialnoisy signals were labeled for excluding glucose measurements withappertaining corrupted HRV. The HRV signal was analyzed with a five min,90% overlapping sliding window calculating typical derived measuresdescribing HRV; heart rate, SDNN (Standard deviation of all NNintervals), SDANN (Standard deviation of the averages of NN intervals inall 5 min segments of the entire recording), pNNx (Proportion of pairsof adjacent NN intervals differing by more than 50 ms), RMSSD (Thesquare root of the mean of the sum of the squares of differences betweenadjacent NN intervals), VLF (Power in very low frequency range, <0.04Hz), LF (Power in low frequency range, 0.04-0.15 Hz), HF (Power in highfrequency range, 0.15-0.4 Hz), TP (total power of all frequencies),LF/HF (ratio of LF and HF), entropy.

The CGM signal was spline resampled to remove short periods withdropouts. Dropouts defined as periods with no measurements shorter than15 minutes. Single measurement of glucose (SMG), such as blood plasmaglucose or self-monitoring of blood glucose levels below 3.9 mmol/L (70mg/dL) were labeled as a hypoglycemic event and otherwise as euglycemia.Glucose spot measurements with corresponding CGM readings showing adiscrepancy of 8 mmol/L (144 mg/dl) or more were considered as erroneousdata in either the CGM or spot measurement and they were thereforeexcluded from further analysis. Furthermore, spot measurements withintwo hours after a hypoglycemic event were excluded since such an eventmay affect the heart rate during the recovery phase. FIG. 13 shows CGMreadings with corresponding SMG readings from one participant. Thedashed line illustrates the threshold for labeling SMG reading as eitherhypoglycemic or euglycemic.

Pattern Classification:

We developed a pattern classification method to predict singlemeasurements of glucose (SMG) into one of the two classes: being withinthe range of hypoglycemia, or euglycemia. The method was based onextracting features from the CGM signal and the derived HRV signal priorto the SMG. A classification model was applied, using features thatproduced the best prediction model. The approach is illustrated in FIG.14, where data in a 60 min prediction window 10 min prior to the SMG hasbeen used to extract features for prediction. In short, we useddifferent time intervals within the prediction window to calculateseveral derived features from the CGM and the corresponding HRV signals.The nature of this approach results in a large number of features. Toeliminate uninformative features, we used a ranking and correlativemethod where the receiver operating characteristic (ROC) for everyfeature was calculated. The result was weighted based on the correlationwith higher-ranking features. The 40 most informative features were keptand the rest were discarded. To find a subset of the most informativefeatures for model inclusion, we used forward selection and concurrentlya 10-fold cross validation. The model used for classifying the patternswas based on logistic regression classification.

Performance:

For evaluation of the hypothesis that HRV could add information in theprediction of hypoglycemia, we assessed and compared three differentmodels; (i) one model (CGM) containing only the raw information from theCGM; (ii) one model (CGM*) containing features derived from the CGMsignal in the prediction window and (iii) one model (CGM+HRV) containingboth features derived from CGM and HRV. The classification performancewas evaluated by sample-based sensitivity and specificity along with ROCand absolute number of true positives, true negatives, false positivesand false negatives for a chosen best model. Each SMG reading waspredicted as either hypoglycemic or euglycemic, and the truth of eachclassification was calculated subsequently. Calculation of the differentmodel (i-iii) performances was based on the prediction window starting0-30 min prior to the SMG readings. Hence, a prediction window starting30 min prior to the reading will yield a 30-min prediction interval.

Results

A total of 12 hypoglycemic events and 237 euglycemic SMG readings wereincluded in the 21 datasets. For a 20 min prediction of the SMG readingthe: (i) CGM model had a ROC AUC of 0.69 with a correspondingsensitivity of 100% and a specificity of 69%. The CGM* model (ii)yielded a ROC AUC of 0.92 with a corresponding sensitivity of 100% andspecificity of 71%. (iii) The CGM+HRV model yielded a ROC AUC of 0.96with a corresponding sensitivity of 100% and specificity of 91%. Therelative and absolute numbers for the 20 min prediction are seen inTable 3 and the corresponding ROC for the three models are depicted inFIG. 15, which shows the comparison of the (ii) CGM* model and the (iii)CGM+HRV model with varying prediction times. FIG. 15 shows theperformance (ROC AUC and Specificity) of the models ii and iii as afunction of prediction time. Such that a prediction of 30 min will give,a 30 min forecast. The CGM+HRV model is obtaining a higher specificitywhen sliding prediction time from 0 to 30 min, whereas the CGM* model issteadily losing prediction power. The difference between the models inthe time dependent analysis is significant (p<0.05).

TABLE 3 MODEL SEN SPE AUC TP TN FP FN (I) CGM 100% 68% 69% 12 161 76 0(II) CGM* 100% 71% 92% 12 168 69 0 (III) CGM HRV 100% 91% 96% 12 216 210 Performance represented as sensitivity (SEN), specificity (SPE), areaunder curve (AUC), true positive (TP), true negative (TN), falsepositive (FP) and false negative (FN)—with a prediction of 20 minues.Performance compared between that of CGM (current reading), CGM*algorithm with features from the CGM and CGM + HRV algorithm withfeatures from both CGM and HRV.

Example 3: Closed-Loop System During Daily Living

In the closed-loop system the insulin pump/CGM device controls when todose with insulin and glucagon to prevent hypoglycemia. Using aclosed-loop system as describing in this application at all time wouldbe ideally, but may not be possible during daily living. Device safetyissues during automated delivery of medication may be issue during dailyliving. CGM, Heart rate measurement device and insulin pump must workindependently and as plug-and-play and connect automatically andsecurely with wireless body area network (or similar technology) everytime the devices are in range of each other. Minor devices failures e.g.low battery in heart rate monitor, must not affect the improveddetection of low blood glucose in CGM device. According to the result inExample 2 Table 3. The CGM* model (ii) yielded a ROC AUC of 0.92 with acorresponding sensitivity of 100% and specificity of 71%. (iii) TheCGM+HRV model yielded a ROC AUC of 0.96 with a corresponding sensitivityof 100% and specificity of 91%.

Therefore, we developed a pattern classification method to predictsingle measurements of glucose (SMG) into one of the two classes: beingwithin the range of hypoglycemia, or euglycemia. The method was based onextracting features from the CGM the pattern prior to the SMG. Aclassification model was applied, using features that produced the bestprediction model. The approach is illustrated in FIG. 14, without HRV,where data in a 60 min prediction window 10 min prior to the SMG hasbeen used to extract features for prediction. In short, we useddifferent time intervals within the prediction window to calculateseveral derived features from the CGM signals. The nature of thisapproach results in a large number of features. To eliminateuninformative features, we used a ranking and correlative method wherethe receiver operating characteristic (ROC) for every feature wascalculated. The result was weighted based on the correlation withhigher-ranking features. The 40 most informative features were kept andthe rest were discarded. To find a subset of the most informativefeatures for model inclusion, we used forward selection and concurrentlya 10-fold cross validation. The model used for classifying the patternswas based on logistic regression classification.

In order to achieve the maximum protection and the possibility topredict low blood glucose both the CGM and HR must be recorded inreal-time and the algorithm will automatically use both measurements.However, if the heart rate measurement device is removed the algorithmwill continue to work and only use the CGM measurements.

Case Story

Patient J is an active young person with type 1 diabetes. He uses acontinuous glucose monitoring device (CGM) on a daily basis. As shown inFIG. 15A, today his glucose levels have been alternating—and at thispoint in time (T) his blood glucose has declined but is still within thenormal range. Should Patient J do something to prevent additionaldecline or is the declining blood glucose within normal dailyvariations?

As shown in FIG. 15B, Patient J is lucky he uses a smart watch thatenables continuous heart rate (HR) monitoring device. The CGM device,with the algorithm installed and HR data (HRV) are connectingautomatically and securely with wireless body area networks (or similartechnology) every time Patient J takes his watch on. In order to achievethe maximum protection and the possibility to predict low blood glucoseboth the CGM and HR are be recorded in real time. However, the CGM mayalso use the algorithm alone to just to detect low blood glucose. Ifthere is an error in Patient J's watch e.g. low battery, then thealgorithm will continue to operate and improve detection of low bloodglucose in CGM device as shown in table 3 of the application.

As shown in FIG. 15C, already obtained CGM and HR data compriseinformation about the type of decline Patient J is experiencing.Therefore, as shown in FIG. 15D, Patient J has the tools to determinewhether the blood glucose decline could lead to an episode of severe andpotentially life threatening hypoglycemia.

As shown in FIG. 15E, the preHypo algorithm uses this historicallyobtained data from Patient J's CGM and HRV device to predict the glucosewaveform. It is all done automatically by his smart-watch (and/or CGMdevice). The data is filtered and processed to obtain a sequence ofmathematically derived features.

The patterns from Patient J's data resemble pattern from other data,which have led to a hypoglycemic episode. Therefore, Patient J's smartphone flags Patient J, alarming him that he should be cautious. This isdone by triggering a customized and personalized alarm from Patient J'ssmart watch. Thereby enable Patient J to intervene by timely drinking oreating sugary fluids (juice) or food. As shown in FIG. 15F, after foodintake the preHypo algorithm will detect the raising blood sugar andgive a personalized feedback to Patient J.

If Patient J had used his closed-loop system all this had happenedautomatically without alarm or need for Patient J to intervene by timelydrinking.

Point-of-Care Device

In an embodiment the point-of-care device disclosed herein may becombined with sensors and electronics so that the device is able tomeasure and control one or more of the sensors, shown in table 4, e.g.EMG or EEG sensors.

In an embodiment the sensor and measurement listed in table 4 arewearables and can be used in combination with the cardio reflex tests.

In an embodiment the point-of-care device disclosed herein may becombined with a CGM device and may be used for calibration of theAlgorithm which is installed on CGM devices. Furthermore it enables CGMmeasurements in combination with cardioreflex tests.

Table 4 provides a list of measurements that may be used in concert withHRV calculated from ECG recordings.

TABLE 4 Examples of sensors/measurements that are link to autonomicnervous system EEG-electroencephalogram Nerve conductionEMG-Electromyography Sudomotor function-Bioimpedance measurement Pulsewave velocity Blood pressure Breathing (sounds and rate) Motiondetection-accelerometer List of biomarkers or biomarker surrogates usedfor detection/prediction for physiological events/conditions(EEG-electroencephalogram; EMG-Electromyography;ECG-Electrocardiography).

FIG. 17 show a block diagram for the point-of-care device. The devicehas interchangeable and independent bio-electrodes forming a part of thehousing of the device. These bio-electrodes can be used to measurebio-impedance or ECG from the hand for the person being examined. Duringimpedance measurement an alternating current is supplied to the tissuethrough one or more of the electrodes. A modulated bio-impedance signalis recorded using one or more separated electrodes. The pulse wavesignal is demodulated, sampled, and processed further in a digitalsignal processor (DSP). The signal from the current-generator is fedback to a contact-detection circuit that, based on the amplitude of thevoltage needed to supply the alternating current, can detect if theelectrodes have poor contact and signal this to the operator via the DSPand the display. External bio-electrodes may be connected to the devicefor impedance plethysmography measuring and for up to 12-Lead ECGmeasurement. External sensors that are shown in table 1 may be connectedwith the device via Bluetooth, wireless body sensor networks or similartechnology.

Oscillometric blood pressure measurements using cuffs are inappropriatein the tests of autonomic nervous system. The main goal with autonomiccardioreflex tests is to measure a sudden change in blood pressure inresponse to external stimuli (e.g. form laying to standing, deepbreathing, forced expiration). During the Valsalva maneuver the patientforcefully exhale for only 15 seconds through a mouthpiece and breathenormally hereafter for 45 seconds. During the test from laying tostanding form a transient decrease in blood pressure and increase inheart rate due to translocation of blood from the central intravascularcompartment to the veins. The translocation of blood reduces venousreturn to the heart, and consequently further induces stroke volume andcardiac output. This activates the arterial baroreceptors with anincrease in heart rate and total peripheral resistance. In healthysubjects, the heart rate and blood pressure reach normal homeostaticlevels after approximately 30 seconds. During deep breathing,inspiration causes a reduction in intrathoracic and abdominal pressurewhich increases venous return to the heart (preload) resulting inincreased stroke volume and heart rate. During expiration, venous returnto the heart is reduced due to a reduction of intrathoracic andabdominal pressure. This decreases cardiac preload and results indecreased stroke volume and heart rate. The above described tests areperform during 1 minute each and the slow and discomfort cuffmeasurement is therefore an inappropriate measuring technique and maystress the patient unnecessary during the cardiovascular reflex tests.

Changes in blood pressure may be measured continuously using the pulsewave signals derived from photo or impedance plethysmography recordings.These measuring techniques can be built-in a watch (smart-watch), Velcroband or attach to the patient with an adhesive substance.

In an embodiment the pulse wave signal is measured by the measurablechange in electrical impedance when the pulse wave moves though thearteries. Impedance plethysmography includes the use of two or morebio-potential electrodes, platinum electrodes, which are attached to theskin surface around the measuring object. The electrodes may afterwardsare be connected with wires or wireless to the actual measuringapparatus.

In an embodiment two or more impedance or photo plethysmography sensorsare attached to e.g. the arm for the patient with a known distancebetween the two points of measurement which enables the measurement ofpulse wave and pulse wave velocity in combination with the cardioreflextests.

In an embodiment detailed analysis of the pulse wave may be performedusing one or more of analysis tools such as, of in various embodiments,include, for example, differentiation, averaging, calculation of slope,ratios of instantaneous values, standard deviation, skewness, regressioncoefficients, slopes of regression ratios, and standardized moment, areaunder the curve, Poincare Plots, Nonlinear analysis, time-frequencyanalysis and performed in time domain or frequency domain. Power spectradensity may then be estimated using parametric or non-parametric modelssuch as, for example, Welch's method, auto regression, periodogram,Bartlett's method, autoregressive moving average, maximum entropy,least-squares spectral analysis, and so forth.

In an embodiment impedance or photo plethysmography sensors are combinedwith 3-Axis accelerometer at each measuring point to record and removemovement artifacts from the signal for interest.

By combining real-time blood pressure measurement and ECG measurement.It possible to measure the so-called baroreflex sensitivity (BRS). BRSis a measure of heart and cardiovascular autonomicreflection/Pressure-buffer system, which main task is to regulate andkeep blood pressure within narrow limits.

BRS is a direct measurement of the Pressure-buffer system and is definedas the change in R-R interval in the ECG due to change in systolic bloodpressure and measured in [ms/mm Hg]. A BRS value of 10 ms/mmHg thusmeans that systolic blood pressure is increased by 1 mmHg, at anextension of the RR interval of 10 ms. In young healthy individuals isbaroreflex sensitivity between 15-20 ms/mmHg.

FIGS. 18A-18B show an example for the handheld point-of-care device.FIG. 18A shows the intact device and B shows the device after removal ofcover 1803 and the round bio-electrodes 1801. The bio-electrodes areconnected to the electronics with the connectors 1804. The userinterface includes buttons 1806 and the display 1802. The tube ofexternal mouthpiece is connected to tab 1805.

In an embodiment, bio-potential electrodes may be used for collectingECG data. The bio-potential measurements, e.g., a voltage produced by atissue of the body (e.g., a muscle tissue during a contraction) may beperformed with conductive or non-conductive electrodes. Advantageously,non-conductive electrodes or capacitive electrodes do not need directcontact between skin and electrodes, thereby saving the time needed toexpose and prepare the contact area when measuring with conductiveelectrodes. Non-conductive electrodes, however, may be more sensitive toeffects of motion than conductive electrodes.

Bio-potential electrodes may consist of multiple layers of metal. Forexample, a first layer may be optimized to bond the housing of thedevice (e.g. Cu on ABS plastic), a second layer may be primer electrodematerial (e.g., silver), and a third layer may be an optimization of theelectrode material for improving the impedance matching betweenelectrodes and the measuring objectives. In an embodiment with a silverprimer electrode, the electrode may comprise silver-silver chloride(Ag/AgCl). In one embodiment the first binding layer may not be present.In one embodiment the third layer may be a disposable tape.

In various embodiments, the bio-potential-electrodes can be incorporatedor embodied into any material like metal or foam material such asrubber, textile, or plastic. For optimized performance of the electricalproperties of electrodes it may be advantageous to add noble colloidalmetal (e.g. silver, gold or platinum) particles suspended in a liquiddirectly to the bio-potential electrodes, or into the conducting gel, orby applying it directly to the skin of the subject before examinations.In such embodiments, the colloidal metal may improve the signal/noiseratio by reducing the skin impedance. Dry and/or old skin creates highimpedance which makes it difficult to acquire good readings. The use ofcolloidal metal may eliminate the need for preparation of the patients'skin by lowering skin impedance.

In one embodiment the colloidal metal or other conducting material likechloride gel or a combination with colloidal and chloride may also beadded to the adhesive patch from standard ECG electrode.

In one embodiment the bio-potential electrodes is an integral part ofthe apparatus and thus has given great freedom of design. This isachieved according to the present invention with the use of surfacecoating technology like PVD (Physical Vapor Deposition) coating.

The surface coated bio-potential electrodes in combination with the useof colloidal metal is not limited to ECG electrodes described herein,and many other applications of biological electrode systems such asbiomonitoring of impedance plethysmography, electroencephalography(EEG), and Electromyography (EMG) may use such electrodes. Furthermore,the bio-potential electrodes may be integrated into exercise equipmentor controllers for e.g. game consoles.

FIGS. 18C-18E show details of the electrode 1801. The electrode 1801 hasat least two sub-electrodes 1811 and 1813. The sub-electrodes 1811 and1813 are separately addressable. The sub-electrodes 1811 and 1813 may beelectrically separated by an insulating portion 1812. The electrode 1801may have electrical contacts 1814 for electrically connecting thesub-electrodes 1811 and 1813 to the rest of the point-of-care device. Inthe example shown in FIGS. 18C-18E, the sub-electrodes 1811 and 1813 mayinclude electrically conductive films on an electrically insulatingsubstrate (e.g., plastic). The insulating portion 1812 may be a portionof the substrate not coated with an electrically conductive film. Thesub-electrodes 1811 and 1813 may include partial cylindrical films. Asshown in FIG. 18E, the sub-electrodes 1811 and 1813 may be a film ofAgCl supported on the substrate with an adhesive in between. Theadhesive is for attaching the film of AgCl to the substrate. An exampleof the adhesive is a layer of copper. The film of AgCl may be made bydepositing a film of Ag and chlorinating the film of Ag. It is possiblethat a layer of unchlorinated Ag exists under the film of AgCl. Theelectrode 1801 may be disposable.

The sub-electrodes of the two electrodes 1801 collectively can measureimpedance. FIG. 19 shows an elementary sketch of a measurement ofimpedance for measurement of pulse wave. In FIG. 19, 1901 indicates across section of a measured object, e.g., in form of a segment of ahuman forearm, and 1902 show a superficial artery. On the surface of theobject of measurement, which, in the example, is the surface of theperson's skin, sub-electrodes 1903, 1904, 1907 and 1908 are placed. Thesub-electrodes 1904 and 1907 are connected to a signal detection circuit1905, while sub-electrodes 1903 and 1908 are connected to a signalgeneration circuit 1906. In practice, the signal generation circuit 1906is a source of electrical current, where alternating current ispreferred, while the signal detection circuit is a voltage detector.During a measurement, the signal, being the alternating current, whichfrom 1906 is penetrating the measured object 1901, as well as theresulting voltage, which is registered in the detection circuit 5 isknown. With knowledge of the current through, as well as the voltageacross the object of measurement the impedance can simply be derivedbased on the formula R=V/I, where R is the impedance in the volume ofmeasurement, which is covered by the measurement setup, V is thevoltage, which is measured in 1905 and I is the current, which isgenerated from 1906. In the electrode 1801 shown in FIGS. 18C-18E, eachhand of the patient touches both sub-electrodes of the electrode 1801when the patient holds the device.

The cubical content, which is defined by the measuring set-up, isprimarily dependent of the mutual location of the electrodes and theirphysical extent. As the volume of blood in the superficial artery underthe electrodes changes due to the pulse wave, the average impedancedetected by the two medial electrodes also change. Because the blood,which flows through the artery, has a characteristic impedance differentthan the nearby tissue, the pulse wave in the artery can be registeredin the signal detector 1905.

Disposable Respiratory Device for Cardioreflex Tests

FIG. 20 shows an example of the mouthpiece. In FIG. 20, 2001 is the airinlet for receiving expired air from a patient. The air inlet isconnected to an interior cavity. The tube is connected to the mouthpiecevia a first air outlet 2002 and pressurized air are guided from thefirst air outlet to a pressure sensor of said the device and describedin FIG. 19. The mouthpiece also has an air leak 2003 for building uppressure in the interior cavity. The mouthpiece may have a second airoutlet 2005 that may be blocked by a movable part, which makes itpossible to perform both the Valsalva maneuver or deep breathing testwith the same mouthpiece.

The mouthpiece may be operated by the patient without using his/herhands. Thus, the patient may operate the mouthpiece while his/her handsare positioned on for example a pair of ECG electrodes.

The mouthpiece may be connected to an associated medical device in thatpressurized air should be guided from the first air outlet to a pressuresensor of said other medical device. Said medical device may furthercomprise ECG electrodes so as to facilitate simultaneous measurements ofexhale pressure and heart beat. Such simultaneous measurements areessential when carrying out a Valsalva maneuver or deep breathing tests.

When the movable is positioned to block the second air outlet for theValsalva maneuver, the air leak 2003 can help to avoid closure of theglottis.

The mouthpiece may further comprise a guide for guiding the moveablepart from the second air outlet to the interior cavity of the first airoutlet in response to the patient's inhalation of air through at leastpart the disposable respiratory device. The guiding means may provide onan interior surface portion of a housing of the mouthpiece, preferablyforming an integral part of the interior surface portion of a housing ofthe mouthpiece. The housing of the mouthpiece may comprise an injectionmouldable material, such as an injection mouldable polymer material.

The moveable part may comprise a substantially spherical element,preferably comprising an injection mouldable material, such as aninjection mouldable polymer material.

FIGS. 21 to 23 shows different designs of the mouthpiece. In FIG. 21,the mouthpiece has a movable part that can be removed from the air inlet2001 by twisting. In FIG. 22, the mouthpiece has a movable part that canbe removed from the air inlet 2001 by breaking the movable part off. InFIG. 23, the mouthpiece has a movable part that is a breakable orremovable seal. When the movable part is removed, an opening is createdand the opening is connected to the air inlet 2001.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

What is claimed is:
 1. An electrode comprising: at least a firstsub-electrode and a second sub-electrode; wherein the firstsub-electrode and the second sub-electrode are separately addressable;wherein the first sub-electrode and the second sub-electrode areseparated by an insulating portion; wherein the electrode is configuredto collect ECG data and to measure impedance from a person's hand incontact with both the first sub-electrode and the second sub-electrode.2. The electrode of claim 21, further comprising electrical contactsconfigured to electrically connect the first sub-electrode and thesecond sub-electrode to a point-of-care device.
 3. The electrode ofclaim 21, wherein the first sub-electrode and the second sub-electrodecomprise an electrically conductive film on an electrically insulatingsubstrate.
 4. The electrode of claim 23, wherein the electricallyinsulating substrate is plastic.
 5. The electrode of claim 23, whereinthe insulating portion is a portion of the substrate not coated with anelectrically conductive film.
 6. The electrode of claim 21, wherein thefirst sub-electrode and the second sub-electrode comprise partialcylindrical films.
 7. The electrode of claim 21, wherein the firstsub-electrode and the second sub-electrode comprise a film of AgClsupported on a substrate with an adhesive in between.
 8. The electrodeof claim 21, wherein the adhesive is a layer of copper.
 9. A mouthpiececonfigured to collect exhaled air from a human, comprising: an inletconfigured to receive the exhaled air; an opening on the inlet; amovable part; wherein removal of the movable part from the inlet exposesthe opening.